---
title: "Coronavirus en Peru"
author: "Jesus Miguel Quispe Quispe"
output: 
  flexdashboard::flex_dashboard:
    theme: yeti
    orientation: rows
    # social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: scroll
---

Sidebar {.sidebar}
=====================================


Proyecto de analisis descriptivo del sars-cov, se compone por :

 1. Resumen
 2. comparacion
 3. Mapa
 
referencia: https://www.statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/

```{r setup, include=FALSE}
#------------------ Packages ------------------

#NOTA :
#1.orientation , orden de cajas

#2. páginas
## columnas o filas
### cajas

#------------------------------------ con doble-- arriba y abajo creas plantillas las cuales pueden editarse insertando cajas en orientacion vertical o orizontal, pero todo esta dentro de estas plantillas que empiezan con #-------
#-------
#-------------------------------------

#4. temas
#"default", "cerulean", "journal", "flatly", "darkly", "readable", "spacelab", "united", "cosmo", "lumen", "paper", #"sandstone", "simplex", "yeti"


library(flexdashboard)
library(coronavirus)
library(dplyr)
library(tidyr)
data(coronavirus)
#update_datasets()
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "blue"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
recovered <-"green"



#------------------ Data ------------------
df <- coronavirus %>%
      filter(country == "Peru") %>%
      group_by(country, type) %>%
      summarise(total = sum(cases)) %>%
      pivot_wider(
      names_from = type,
      values_from = total
      ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  arrange(-confirmed) %>%
  ungroup() %>%
  mutate(country = if_else(country == "United Arab Emirates", "UAE", country)) %>%
  mutate(country = if_else(country == "Mainland China", "China", country)) %>%
  mutate(country = if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  mutate(country = trimws(country)) %>%
  mutate(country = factor(country, levels = country))

df_daily <- coronavirus %>%
  filter(country == "Peru") %>%
  group_by(date, type) %>%
  summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total) %>%
  arrange(date) %>%
  ungroup() %>%
  #dplyr::mutate(active = confirmed - death - recovered) %>%
  mutate(active = confirmed - death) %>%
  mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    # recovered_cum = cumsum(recovered),
    active_cum = cumsum(active))
df1 <- coronavirus %>% dplyr::filter(date == max(date))
```





---
# 1era ventana
--- 

Resumen
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}
library(shiny)
valueBox(
  value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
  caption = "Numero total de casos confirmados",
  icon = "fas fa-user-md",
  color = confirmed_color
)


```

### Ejemplo
```{r}

valueBox(format(sum(df$recovered), big.mark = ","), 
caption = paste("Numero de casos recuperados", actionButton("button1", " ", style = "background-color:rgba(39, 128, 227, 0.0); border-color:rgba(39, 128, 227, 0.0); position: absolute; overflow: hidden; left: 0px; top: 0px; right: 0px; bottom: 0px; width:100%")),
icon = "fa-thumbs-up", 
color = "success")
```

















### death {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Casos de muerte (tasa de mortalidad)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


Row
-----------------------------------------------------------------------

### **Casos acumulativos diarios por tipo** (solo Peru)
    
```{r}
library(plotly)
plotly::plot_ly(data = df_daily) %>%
  add_trace(
    x = ~date,
    # y = ~active_cum,
    y = ~confirmed_cum,
    type = "scatter",
    mode = "lines+markers",
    # name = "Active",
    name = "Confirmados",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Death",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  add_annotations(
    x = as.Date("2020-02-29"),
    y = 1,
    text = paste("Primer caso"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  add_annotations(
    x = as.Date("2020-03-11"),
    y = 1,
    text = paste("Primera muerte"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  # plotly::add_annotations(
  #   x = as.Date("2020-03-18"),
  #   y = 14,
  #   text = paste(
  #     "New containment",
  #     "
",
  #     "measures"
  #   ),
  #   xref = "x",
  #   yref = "y",
  #   arrowhead = 5,
  #   arrowhead = 3,
  #   arrowsize = 1,
  #   showarrow = TRUE,
  #   ax = -10,
  #   ay = -90
  # ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Numero de casos acumulado"),
    xaxis = list(title = "Fecha"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```





---
# 2da ventana
--- 

Analisis comparativo AL
=======================================================================


row {data-width=400}
-------------------------------------


### **Nuevos casos diariamente confirmados**
    
```{r}

library(dplyr)
library(plotly)
library(tidyr)
daily_confirmed <- coronavirus %>%
    filter(type == "confirmed") %>%
    filter(date >= "2020-02-29") %>%
    mutate(country = country) %>%
    group_by(date, country) %>%
    summarise(total = sum(cases)) %>%
    ungroup() %>%
     pivot_wider(names_from = country, values_from = total)
#----------------------------------------
# Plotting the data
daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~Peru,
    type = "scatter",
    mode = "lines+markers",
    name = "Peru"
  ) %>%
   plotly::add_trace(
     x = ~date,
     y = ~Argentina,
     type = "scatter",
     mode = "lines+markers",
     name = "Argentina"
   ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Ecuador,
    type = "scatter",
    mode = "lines+markers",
    name = "Ecuador"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Bolivia,
    type = "scatter",
    mode = "lines+markers",
    name = "Bolivia"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Numbero de nuevos casos confirmados"),
    xaxis = list(title = "Fecha"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```
 
 row {data-width=400}
-------------------------------------

### **Distribucion de casos por tipo**

```{r daily_summary}

library(dplyr)
library(plotly)

df_EU <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  filter(country == "Peru" |
    country == "Argentina" |
    country == "Ecuador" |
    country == "Bolivia") %>%
 group_by(country, type) %>%
summarise(total = sum(cases)) %>%
  pivot_wider(
    names_from = type,
    values_from = total) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
  data = df_EU,
  x = ~country,
  # y = ~unrecovered,
  y = ~ confirmed,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Confirmados",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~death,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Muertos",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total de casos"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )


#Ejemplo de crear ventanas 
#Map
#=======================================================================

### **World map of cases** (*use + and - icons to zoom in/out*)

```